Overcoming 5 Hurdles to Using Jupyter Notebooks for Data Science, by the JetBrai...
summary
Five concrete problems get concrete solutions: per-notebook environment manager that isolates and saves dependency files; attached files plus reusable DB connections and native SQL cells with completion and auth-server–stored credentials; notebook editor with PyCharm/DataGrip coding features; real-time collaboration like Google Docs and shareable interactive “publish” reports; and a language-agnostic reactive mode (built on creo) that enforces top-down recalculation and claims to make notebooks reproducible, available in cloud and self-hosted Datalore.
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chapters (9)
- 0:00Intro
- 0:45Why Jupyter
- 1:16Environments
- 3:25Multiple Data Providers
- 6:12Writing Code Without Idea Support
- 8:06Sharing Your Work
- 12:29Reproducibility
- 15:36Questions
- 22:07Conclusion
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